Point of view
From pilot to production: scaling generative AI in the enterprise
Generative AI pilots are easy to start and hard to scale. Five decisions separate the programs that reach production from those that stall.
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Point of view
From dashboards to decisions: making analytics adoption stick
Many organizations have more dashboards than ever and still struggle to make data-driven decisions. Adoption is a design and leadership challenge, not a tooling problem.
Point of view
Data foundations first: why AI programs stall without them
AI ambitions often collide with fragmented, poorly governed data. You do not need a perfect data estate to start — but you do need the right foundations for each use case.
Point of view
AI agents in the enterprise: where they create value today
AI agents can plan, use tools and complete multi-step tasks. The opportunity is real — but so is the need for clear boundaries, evaluation and human oversight.
Point of view
AI in healthcare operations: high-value, lower-risk places to start
Clinical AI attracts the headlines, but some of the clearest near-term opportunities in healthcare lie in operations. Where to start, and how to manage risk, privacy and trust along the way.
Point of view
RAG or fine-tuning? Choosing the right approach for enterprise generative AI
Retrieval-augmented generation and fine-tuning solve different problems. Understanding the difference helps teams avoid expensive detours and build generative AI solutions that stay accurate as the business changes.
Point of view
Data quality and DataOps: the foundation of trusted analytics
When people stop trusting the numbers, analytics and AI investments lose their value. DataOps brings engineering discipline to data so quality problems are prevented, caught early and fixed fast.
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